Quantum-classical simulation methods for hydrogen transfer in enzymes: a case study of dihydrofolate reductase

A variety of theoretical approaches have been used to investigate hydrogen transfer in enzymatic reactions. The free energy barriers for hydrogen transfer in enzymes have been calculated using classical molecular dynamics simulations in conjunction with quantum mechanical/molecular mechanical and empirical valence bond potentials. Nuclear quantum effects have been included with several different approaches. Applications of these approaches to hydride transfer in dihydrofolate reductase are consistent with experimental measurements and provide significant insight into the protein conformational changes that facilitate the hydride transfer reaction.     

Linking latest knowledge of injury mechanisms and spine function to the prevention of low back disorders.

While several sophisticated scientific approaches have been employed to understand low back function and injury mechanisms, very few have been broadly used to develop and justify injury prevention strategies. This paper looks beyond the linked segment model, and the lessons learned from this biomechanical approach, to consider the application of more sophisticated approaches. These include modelling approaches with greater anatomical and biological fidelity, fusing the lessons learned from the areas of tissue mechanics and concepts of spine stability, together with some studies that have examined several characteristics including psychosocial, physiological and personal variables. The objective is to better link recently discovered mechanisms of injury and spine tissue health with injury risk reducing approaches.     

The linkage of actin to non-erythroid membranes

The question of how actin filaments are attached to membranes is of central importance to an understanding of how actin gives rise to shape and movement in cells. A number of approaches to this question have been taken, but there have been few definitive answers. Some of the limitations of these approaches are discussed, as well as possible avenues for overcoming them.     

Chemical genetic approaches for the elucidation of signaling pathways

New chemical methods that use small molecules to perturb cellular function in ways analogous to genetics have recently been developed. These approaches include both synthetic methods for discovering small molecules capable of acting like genetic mutations, and techniques that combine the advantages of genetics and chemistry to optimize the potency and specificity of small-molecule inhibitors. Both approaches have been used to study protein function in vivo and have provided insights into complex signaling cascades.     

Parametric and nonparametric FDR estimation revisited

Nonparametric and parametric approaches have been proposed to estimate false discovery rate under the independent hypothesis testing assumption. The parametric approach has been shown to have better performance than the nonparametric approaches. In this article, we study the nonparametric approaches and quantify the underlying relations between parametric and nonparametric approaches. Our study reveals the conservative nature of the nonparametric approaches, and establishes the connections between the empirical Bayes method and p-value-based nonparametric methods. Based on our results, we advocate using the parametric approach, or directly modeling the test statistics using the empirical Bayes method.     

Chemical biology and bacteria: not simply a matter of life or death

Chemical biological approaches to understanding bacteria have largely been confined to screening for antibiotics. More complex phenotypes, such as virulence, have largely been studied using bacterial genetics. However, it has recently become clear that these two methods are complementary and that combining chemical biologic and genetic approaches to studying bacteria brings new power to old problems.     

计算识别microRNA及其靶基因

小RNA的发现为基因调控系统研究提供了新的方向。在多数物种中已经发现了大量的小RNA。这一领域已经成为了近来研究的热点,在研究起始阶段,计算学方法已经成为实验研究中不可或缺的工具,许多发现是由生物学实验与计算学方法共同合作来完成的。在这篇综述中,我们总结了前人关于小RNA及其靶基因识别的理论知识。最后,讨论了关于预测小RNA及其靶基因的计算学方法和相关软件。     

Advanced Transgenic and Gene-Targeting Approaches

Over the past two decades the techniques associated with the manipulation of the mouse genome have provided a powerful approach toward the better understanding of gene function. Conventional transgenie and gene targeting approaches have been used extensively, and these techniques have been particularly rewarding for neuroscientists. Nevertheless, the traditional approaches toward genome manipulation have certain limitations that diminish their usefulness for studying more sophisticated biological processes. Therefore, variations to these techniques have recently been developed. The improvements are focused on two areas: one provides regulated control of transgene expression using an inducible expression system; and the other provides the opportunity to inactivate genes in specific cells and at predetermined developmental stages with a conditional gene targeting system. This review summarizes the advantages as well as some of the technical difficulties of these new approaches. The application of these advanced approaches in biomedical research, particularly neuroscience, are also discussed.     

A Simulation Study of an Object-Oriented Integration Testbed for Process Planning and Production Scheduling

Many studies on integration of process planning and production scheduling have been carried out during the last decade. While various integration approaches and algorithms have been proposed, the implementation of these approaches is still a difficult issue. To achieve successful implementation, it is important to examine and evaluate integration approaches or algorithms beforehand. Based on an object-oriented integration testbed, a simulation study that compares different integration algorithms is presented in this paper. Separated planning method and integrated planning methods are examined. Also, situations of both fixed and variable processing times are simulated, and useful results have been observed. The successful simulation with the object-oriented integration testbed eventually will be extended to include other new planning algorithms for examining their effectiveness and implementation feasibility.     

The molecular genetic basis of plant adaptation

How natural selection on adaptive traits is filtered to the genetic level remains largely unknown. Theory and quantitative trait locus (QTL) mapping have provided insights into the number and effect of genes underlying adaptations, but these results have been hampered by questions of applicability to real biological systems and poor resolution, respectively. Advances in molecular technologies have expedited the cloning of adaptive genes through both forward and reverse genetic approaches. Forward approaches start with adaptive traits and attempt to characterize their underlying genetic architectures through linkage disequilibrium mapping, QTL mapping, and other methods. Reverse screens search large sequence data sets for genes that possess the signature of selection. Though both approaches have been successful in identifying adaptive genes in plants, very few, if any, of these adaptations' molecular bases have been fully resolved. The continued isolation of plant adaptive genes will lead to a more comprehensive understanding of natural selection's effect on genes and genomes.     

Gene therapy for high-grade glioma

The treatment of high-grade gliomas remains difficult despite recent advances in surgery, radiotherapy, and chemotherapy. True advances may emerge from the increasing understanding in molecular biology and discovery of novel mechanisms for the delivery of tumoricidal agents. In an attempt to overcome this formidable neoplasm, molecular approaches using gene therapy have been investigated clinically since 1992. The clinical trials have mainly been classified into three approaches: suicide gene therapy, immune gene therapy and oncolytic viral therapy. In this article, we review these approaches, which have been studied in previous and ongoing clinical trials.     

Pharmacologic approaches to the treatment of cocaine dependence.

When pharmacologic agents are considered in the treatment of cocaine addiction, the objective of such treatment--sustained abstinence--must be considered. Medication and medical approaches have been disappointing in the treatment of cocaine overdose. The central neurobiologic mechanism(s) involved in cocaine toxicity are poorly understood. Without a cocaine antagonist, pharmacologic approaches have been less than promising in preventing relapse. Various psychoactive medications have been tried in early cocaine abstinence, with some success.     

Overview of some theoretical approaches for derivation of the Monod equation

The Monod equation has been widely applied to describe microbial growth, but it has no mechanistic basis and is purely empirical. Extensive efforts have been dedicated to develop theoretical approaches for derivation of the Monod equation, which can be classified into three major groups, i.e., kinetic, thermodynamic, and substance transport approaches. In this review, four representative approaches are thus discussed. Due to the fact that different assumptions are made in each approach, no universal physical meaning of the Monod constant (K (s)) can be revealed. However, it seems that the Monod constant would be free energy-dependent and have nonequilibrium thermodynamic characteristics.     

Thiol redox proteomics: Characterization of thiol-based post-translational modifications

Redox post-translational modifications on cysteine thiols (redox PTMs) have profound effects on protein structure and function, thus enabling regulation of various biological processes. Redox proteomics approaches aim to characterize the landscape of redox PTMs at the systems level. These approaches facilitate studies of condition-specific, dynamic processes implicating redox PTMs and have furthered our understanding of redox signaling and regulation. Mass spectrometry (MS) is a powerful tool for such analyses which has been demonstrated by significant advances in redox proteomics during the last decade. A group of well-established approaches involves the initial blocking of free thiols followed by selective reduction of oxidized PTMs and subsequent enrichment for downstream detection. Alternatively, novel chemoselective probe-based approaches have been developed for various redox PTMs. Direct detection of redox PTMs without any enrichment has also been demonstrated given the sensitivity of contemporary MS instruments. This review discusses the general principles behind different analytical strategies and covers recent advances in redox proteomics. Several applications of redox proteomics are also highlighted to illustrate how large-scale redox proteomics data can lead to novel biological insights.     

Gene disruption to evaluate the role of fungal candidate virulence genes

Gene disruption is a powerful genetic tool that can define pathogenic or virulence factors. In the past two years gene disruption approaches have been used to identify fungal virulence genes. The capsule genes, an alpha subunit of G protein and certain kinases of Cryptococcus neoformans have clearly been demonstrated to be associated with pathogenicity. In Candida albicans at least four genes involved in hyphal formation have been disrupted and tested for virulence. In other fungi, such as Histoplasma capsulatum, however, more efficient gene disruption methods need to be developed before such approaches can be regularly used for identifying virulence genes.     

Comparative analysis of OFFGel,strong cation exchange with pH gradient,and RP at high pH for first‐dimensional separation of peptides from a membrane‐enriched protein fraction

The analysis and quantitation of membrane proteins have proved challenging for proteomics. Although several approaches have been introduced to complement gel‐based analysis of intact proteins, the literature is rather limited in comparing major emerging approaches. Peptide fractionation using IEF (OFFGel), strong cation exchange HPLC using a pH gradient (SCX‐pG), and RP HPLC at high pH, have been shown to increase peptide and protein identification over classic MudPIT approaches. This article compares these three approaches for first‐dimensional separation of peptides using a detergent phase (Triton X‐114) enriched membrane fraction from mouse cortical brain tissue. Results indicate that RP at high pH (pH 10) was superior for the identification of more peptides and proteins in comparison to the OFFGel or the SCX‐pG approaches. In addition, gene ontology analysis (GOMiner) revealed that RP at high pH (pH 10) successfully identified an increased number of proteins with “membrane” ontology, further confirming its suitability for membrane protein analysis, in comparison to SCX‐pG and OFFGel techniques.     

Assessing Global Adoption of One Health Approaches

Transdisciplinary One Health (OH) approaches have been rediscovered as a promising tactic for addressing complex health risks at the human-animal-ecosystem interface. However, there is little evidence of widespread adoption of OH approaches as the new operating normal for addressing these complex health issues. We have used a transformational change model as an evaluation tool and part of an overall assessment of the global adoption of OH approaches. This assessment establishes a point of reference for measuring progress toward OH approaches being the new operating normal. Global adoption of OH approaches will require more strategic efforts to build the case (value proposition), recruiting a broader pool of One Health champions, solidifying partnerships and unifying OH efforts.     

A critical examination of stoichiometric and path-finding approaches to metabolic pathways

Advances in the field of genomics have enabled computational analysis of metabolic pathways at the genome scale. Singular attention has been devoted in the literature to stoichiometric approaches, and path-finding approaches, to metabolic pathways. Stoichiometric approaches make use of reaction stoichiometry when trying to determine metabolic pathways. Stoichiometric approaches involve elementary flux modes and extreme pathways. In contrast, path-finding approaches propose an alternative view based on graph theory in which reaction stoichiometry is not considered. Path-finding approaches use shortest path and k-shortest path concepts. In this article we give a critical overview of the theory, applications and key research challenges of stoichiometric and path-finding approaches to metabolic pathways.     

Combinatorial genetic perturbation to refine metabolic circuits for producing biofuels and biochemicals

Recent advances in metabolic engineering have enabled microbial factories to compete with conventional processes for producing fuels and chemicals. Both rational and combinatorial approaches coupled with synthetic and systematic tools play central roles in metabolic engineering to create and improve a selected microbial phenotype. Compared to knowledge-based rational approaches, combinatorial approaches exploiting biological diversity and high-throughput screening have been demonstrated as more effective tools for improving various phenotypes of interest. In particular, identification of unprecedented targets to rewire metabolic circuits for maximizing yield and productivity of a target chemical has been made possible. This review highlights general principles and the features of the combinatorial approaches using various libraries to implement desired phenotypes for strain improvement. In addition, recent applications that harnessed the combinatorial approaches to produce biofuels and biochemicals will be discussed.     

Microbial pathway engineering for industrial processes: evolution, combinatorial biosynthesis and rational design

Microbial pathway engineering has made significant progress in multiple areas. Many examples of successful pathway engineering for specialty and fine chemicals have been reported in the past two years. Novel carotenoids and polyketides have been synthesized using molecular evolution and combinatorial strategies. In addition, rational design approaches based on metabolic control have been reported to increase metabolic flux to specific products. Experimental and computational tools have been developed to aid in design, reconstruction and analysis of non-native pathways. It is expected that a hybrid of evolutionary, combinatorial and rational design approaches will yield significant advances in the near future.